Erik Kusch, PhD Student
Department of Biology
Section for Ecoinformatics & Biodiversity
Center for Biodiversity Dynamics in a Changing World (BIOCHANGE)
Aarhus University
05/02/2021
[Study Group] Bayesian Statistics with the Rethinking Material 1
05/02/2021
[Study Group] Bayesian Statistics with the Rethinking Material 2
Models need to straddle the line between being too
simplistic and being overly complex.
05/02/2021
[Study Group] Bayesian Statistics with the Rethinking Material 3
05/02/2021
[Study Group] Bayesian Statistics with the Rethinking Material 4
Models ought to be regularised (tuned for
desired complexity-accuracy)!
05/02/2021
[Study Group] Bayesian Statistics with the Rethinking Material 5
We can compare models without knowing the absolute truth.
05/02/2021
[Study Group] Bayesian Statistics with the Rethinking Material 6
Difference in
log-score
between
training and
test data
reveals
overfitting
05/02/2021
[Study Group] Bayesian Statistics with the Rethinking Material 7
Regularised priors fair worse in-
sample, but better out-of-sample.
Regularisation effects diminish with increasing sample sizes.
05/02/2021
[Study Group] Bayesian Statistics with the Rethinking Material 8
This is the model we would want to select
because it shows the treatment effect
Model Selection can lead to mis-
identification of causal pathways.